Active power distribution network distributed power flow calculation method and system

Abstract

The application provides a kind of active power distribution network distributed power flow calculation method and system, the method includes: constructing steady-state branch power flow model and the constraint condition corresponding thereto, constructs transformer thermal aging model, respectively constructs first operation model and second operation model about electric vehicle and distributed photovoltaic;Optimization problem model about network side, EV side, PV side is constructed;Define the first decision variable related to active power distribution network and the second decision variable related to electric vehicle and photovoltaic, generate active power distribution network optimal power flow problem and with the first optimization problem;For constraint condition, introduce a reverse inequality function, linearly process reverse inequality function, and introduce penalty term, obtain second optimization problem;According to the improved solving algorithm, the optimization problem model of network side, EV side, PV side is recursively solved.The application can avoid direct interaction of sensitive data, ensure the demand of individual privacy and independent calculation of each participant.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed power flow calculation, and in particular to a distributed power flow calculation method and system for an active power distribution network. Background Art

[0002] To achieve the "dual carbon" goals and a clean and efficient energy system, the proportion of new energy sources, primarily photovoltaics (PV) and wind power, in energy consumption continues to expand, leading to a continued increase in the proportion of electric vehicles (EVs) in distribution network loads. The uncertainty and volatility of new energy sources on the source side and the plug-and-play nature of a large number of EVs on the load side place higher demands on the safe operation of distribution networks. Active distribution networks demonstrate advantages in coordinated control of "source-load" and can effectively address uncertainties on both sides of the "source-load" relationship. Consequently, active distribution networks have been vigorously developed. System operators solve optimal power flow problems to understand system status and derive optimal operating strategies, thereby ensuring the safe and economical coordinated operation of various generation resources and flexible loads in active distribution networks.

[0003] However, the information that can be shared among the various operating entities in an active distribution network is limited, making unified analysis of power flows across the entire network difficult. Therefore, to ensure individual privacy and independence, a distributed computational method for optimal power flows in active distribution networks is urgently needed. This method can enable independent solutions for each entity and safeguard their respective interests. Summary of the Invention

[0004] The purpose of the present invention is to provide a distributed power flow calculation method and system for an active power distribution network, aiming to solve the problem that traditional centralized calculation methods for active power distribution networks are difficult to ensure individual privacy security and relative independence.

[0005] In a first aspect, the present invention provides a method for calculating distributed power flow in an active power distribution network, the method comprising:

[0006] Construct a steady-state branch power flow model of the active distribution network and its corresponding constraints, build a transformer thermal aging model, define electric vehicles and distributed photovoltaics as energy access types for the active distribution network, and construct the first and second operation models for electric vehicles and distributed photovoltaics respectively;

[0007] Constructing an optimization problem model on the network side, the EV side, and the PV side based on the steady-state branch power flow model, the transformer thermal aging model, the first operation model, and the second operation model;

[0008] defining a first decision variable related to the active distribution network and a second decision variable related to electric vehicles and photovoltaics, and generating an optimal power flow problem of the active distribution network and a first optimization problem related to the second decision variable based on the definition results;

[0009] Introducing an inverse inequality function for the constraint condition, performing linear processing on the inverse inequality function, and introducing a penalty term to obtain a second optimization problem;

[0010] The optimization problem models on the network side, EV side, and PV side are recursively solved based on the improved solution algorithm.

[0011] Furthermore, the step of constructing a steady-state branch power flow model of the active power distribution network and its corresponding constraint conditions includes:

[0012] The steady-state branch power flow model is constructed according to the following formula:

[0013] ;

[0014] in, Line Active and reactive power in period t, 、 Line The resistance and reactance on For the line The square of the current amplitude during the period t, 、 are the squares of the voltage amplitudes at nodes j and i during time period t, 、 The active and reactive power of the load of node j in time period t, 、 Line The active and reactive power in time period t, k is the index of the downstream child node of node j, is the set of downstream child nodes of node j, s is the element index in the set, is the set of electric vehicles connected to node j during period t, 、 are the active and reactive outputs of the electric vehicle at node j, is the set of photovoltaics connected to node j during period t, 、 are the photovoltaic active and reactive outputs at node j, 、 is the Lagrange multiplier corresponding to the equality constraint, T is the time period set, N is the node set, is the set of nodes except the root node, It is the only upstream parent node of node j;

[0015] The constraints are constructed according to the following formula:

[0016] ;

[0017] in, 、 are the upper and lower limits of the square of the voltage amplitude, is the upper limit of the square of the current amplitude, L is the line set, and T is the set consisting of the indexes of the optimization time period.

[0018] Furthermore, the steps of constructing a transformer thermal aging model, defining electric vehicles and distributed photovoltaics as energy access types of the active distribution network, and constructing a first operation model and a second operation model for electric vehicles and distributed photovoltaics respectively include:

[0019] The transformer thermal aging model is constructed according to the following formula:

[0020] ;

[0021] in, is the aging acceleration factor of the heat exchanger in time period t, M represents the total number of segments, 、 Respectively The slope and intercept of the segment, is the hottest point temperature of the winding during period t, For the The lower limit of the temperature range of the segment, For the The upper limit of the temperature range of the segment, is the top oil temperature at time t, m is a constant, is the temperature rise of the hottest point of the winding compared to the top oil temperature under rated load, is the square of the current amplitude, is the square of the rated current amplitude, is the Lagrange multiplier corresponding to the constraint, which is used to express the influence of the load at the end of one working cycle T on the life loss of the transformer in the next working cycle, e is the index of the transformer in working state, and E is the set of transformers in working state;

[0022] The first run model is constructed according to the following formula:

[0023] ;

[0024] in, To meet the charging needs of electric vehicles, is the maximum limit of the active power of the electric vehicle, The maximum limit of the apparent power of the electric vehicle;

[0025] The second run model is constructed according to the following formula:

[0026] ;

[0027] in, is the maximum limit of distributed photovoltaic active power, It is the maximum limit of distributed photovoltaic apparent power.

[0028] Furthermore, the step of constructing an optimization problem model on the network side, the EV side, and the PV side based on the steady-state branch power flow model, the transformer thermal aging model, the first operation model, and the second operation model includes:

[0029] For the network side, the final objective function is:

[0030] ;

[0031] in, 、 、 are the price of active power purchased from the root node, the unit cost of reactive power, and the unit cost of transformer life loss, is the active power of line 01 in period t, is the reactive power of line 01 in period t;

[0032] For the EV side, the initial objective function is:

[0033] ;

[0034] For the PV side, the initial objective function is:

[0035] ;

[0036] in, is the node active power price in period t, is the node reactive power price in period t.

[0037] Furthermore, the steps of defining a first decision variable related to the active distribution network and a second decision variable related to electric vehicles and photovoltaics, and generating an optimal power flow problem for the active distribution network and a first optimization problem related to the second decision variable according to the definition results include:

[0038] definition is a first decision variable related to the power distribution network, wherein the first decision variable includes 、 、 、 、 、 、 ;

[0039] definition is a second decision variable related to electric vehicles and photovoltaics, and the second decision variable includes 、 、 、 ;

[0040] The first optimization problem is formulated as follows:

[0041] ;

[0042] in, represents the operating cost of the distribution network, is the feasible region of the first decision variable, is the feasible region of the second decision variable, is the node electricity price matrix, A, B, and D are the coefficient matrices of the equality constraints related to the second decision variable in the steady-state branch power flow model, is the value of the decision variable known to be correlated with the second decision variable, To meet relevant conditions;

[0043] Given , solve the first optimization problem;

[0044] Based on the first optimization problem, we get the optimization problem about y, and solve the optimization problem about y to get :

[0045] ;

[0046] in, 、 are the values ​​solved for the kth and k+1th iterations respectively, To obtain the variable value that minimizes the objective function, represents the operating costs associated with electric vehicles and photovoltaics, is the transpose of the node electricity price matrix at the kth iteration;

[0047] definition 、 is the active and reactive power output of photovoltaic power in time period t at the kth iteration, 、 are the active and reactive outputs of the electric vehicle in the t period at the kth iteration, respectively. The final objective function of the EV side is:

[0048] ;

[0049] The final objective function of the PV side is:

[0050]

[0051] wherein, is a positive scalar parameter at the kth iteration, is the node active power price at time period t at the kth iteration, is the node reactive power price at time period t at the kth iteration.

[0052] Further, the step of introducing a reverse inequality function for the constraint condition, linearly processing the reverse inequality function, and introducing a penalty term to obtain the second optimization problem comprises:

[0053] The expression of the introduced reverse inequality function is as follows:

[0054]

[0055] linearly processing the reverse inequality function and introducing a penalty term to obtain:

[0056]

[0057] wherein, , is the line active and reactive power at time period t obtained at the kth iteration, is the square of the voltage amplitude of node i at time period t obtained at the kth iteration, is the line current amplitude at time period t obtained at the kth iteration, is a penalty term;

[0058] The penalty term can be subjected to a weight factor to obtain the second optimization problem: .

[0059] Further, the step of recursively solving the optimization problem models of the network side, the EV side, and the PV side according to the improved solving algorithm comprises:

[0060] Step 1, starting from , a given initial scheduling strategy is provided, including , , , , an error tolerance is set, and an iteration time limit ;

[0061] ​​​Step 2: Solve the final objective function on the network side based on the given initial scheduling strategy and determine whether the constraints are effective.

[0062] Step 2.1: If the constraint is a valid constraint, get the correct and , go to step 3;

[0063] Step 2.2: If the constraint condition is invalid, set the initial number of inner loops. ;

[0064] Step 2.3: Solve the second optimization problem and determine the error of the kth iteration. Is it greater than ;

[0065] Step 2.4: If ,make , return to step 2.3 to continue solving;

[0066] Step 2.5: If , solve the new network optimization problem model, and obtain and , go to step 3, the new network optimization problem model includes the following expression:

[0067] ;

[0068] Step 3: Get and , solve the final objective function of the EV side and the PV side, obtain the scheduling strategy under the current iteration, and judge whether the scheduling strategy output by the current iteration meets the demand or whether the current iteration reaches the iteration time;

[0069] Step 4: If the requirements are met or the iteration time is reached, the iteration ends. If the requirements are not met and the iteration time is not reached, the scheduling strategy output by the previous iteration is used as the new given scheduling strategy and returns to step 2.

[0070] In a second aspect, the present invention provides a distributed power flow calculation system for an active power distribution network, the system comprising:

[0071] The power flow model construction module is used to construct the steady-state branch power flow model of the active distribution network and its corresponding constraints, build a transformer thermal aging model, define electric vehicles and distributed photovoltaics as energy access types for the active distribution network, and construct the first and second operation models for electric vehicles and distributed photovoltaics respectively;

[0072] An optimization problem model construction module, configured to construct optimization problem models on the network side, the EV side, and the PV side based on the steady-state branch power flow model, the transformer thermal aging model, the first operation model, and the second operation model;

[0073] A decision variable definition module is used to define a first decision variable related to the active distribution network and a second decision variable related to electric vehicles and photovoltaics, and generate an optimal power flow problem for the active distribution network and a first optimization problem related to the second decision variable based on the definition results;

[0074] an optimization problem construction module, configured to introduce an inverse inequality function into the constraint condition, perform linear processing on the inverse inequality function, and introduce a penalty term to obtain a second optimization problem;

[0075] The recursive solution module is used to recursively solve the optimization problem models on the network side, EV side, and PV side according to the improved solution algorithm.

[0076] In a third aspect, the present invention provides a storage medium storing one or more programs, which, when executed by a processor, implement the above-mentioned method for calculating distributed power flow in an active power distribution network.

[0077] In a fourth aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein:

[0078] The memory is used to store computer programs;

[0079] When the processor is used to execute the computer program stored in the memory, the above-mentioned active power distribution network distributed power flow calculation method is implemented.

[0080] Compared with the prior art, the present invention has the following advantages:

[0081] 1. The present invention constructs a steady-state branch flow model of the active distribution network, a transformer thermal aging model, and an operation model for electric vehicles and distributed photovoltaics, and based on these models, constructs an optimization problem model covering the network side, EV side, and PV side. At the same time, the decision variables on different sides are defined and converted into optimization problems. The constraints are then processed and linearized with special functions and penalty terms are introduced to form the final optimization problem. Finally, an improved solution algorithm is used for recursive solution. This hierarchical modeling and optimization solution method naturally fits the distributed computing architecture. Different models and optimization problems can be processed in parallel by different computing nodes in a distributed computing environment, thereby realizing distributed computing. In the processing of each link, reasonable data partitioning and model abstraction are used, and the key parameters required for optimization solution are transmitted only when necessary, avoiding direct interaction of sensitive data, ensuring personal privacy and the independent computing needs of each participant.

[0082] 2. In addition to considering the cost of purchasing electricity, the present invention also considers the cost of transformer aging. In addition, this method establishes an inverse inequality for the error caused by the second-order cone relaxation, and through linearization processing and inner loop iteration, it can gradually reduce the relaxation error while being easy to optimize. At the same time, this method can ensure the executability of the temporary solution obtained in the iterative process, and also makes it possible to dynamically adjust the target parameters and constraints during each iteration to cope with the real-time changing system status. When violations are observed, the optimization problem parameters can be adjusted at any time. In actual engineering, dispatchers will follow a similar iterative update process while ensuring the feasibility of the solution. The scheduling plan is checked. If it is found that some constraints that have not been formalized are not met, these constraints are incorporated into the standardized optimization problem, or the original parameters / constraints are adjusted to ensure feasibility. In day-ahead scheduling, this process will be repeated until a feasible solution is found or time is exhausted. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 This is a flow chart of a distributed power flow calculation method for an active power distribution network proposed in one embodiment of the present invention;

[0084] Figure 2 This is a structural diagram of a distributed power flow calculation system for an active power distribution network proposed in one embodiment of the present invention.

[0085] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0086] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be the common meanings understood by people with ordinary skills in the field to which the invention belongs. The words "including" and similar words used in this article mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0087] like Figure 1 As shown, an embodiment of the present invention provides a method for calculating distributed power flow in an active power distribution network, the method comprising steps S101 to S105, wherein:

[0088] Step S101: Constructing a steady-state branch power flow model of the active distribution network and its corresponding constraints, constructing a transformer thermal aging model, defining electric vehicles and distributed photovoltaics as energy access types for the active distribution network, and constructing first and second operation models for electric vehicles and distributed photovoltaics, respectively;

[0089] It should be pointed out that in this step, the radial topology is first adopted for the active distribution network. Represents a node set, where 0 represents the root node. The root node 0 has no parent node, and the leaf node has no child node. Represents a collection of nodes excluding the root node. The set of transformers E in working state is defined as a subset of the set of lines L.

[0090] In some embodiments, the steady-state branch power flow model and its constraints of the active distribution network can be expressed as:

[0091] ;

[0092] in, Line Active and reactive power in period t, 、 Line The resistance and reactance on For the line The square of the current amplitude during the period t, 、 are the squares of the voltage amplitudes at nodes j and i during time period t, 、 The active and reactive power of the load of node j in time period t, 、 Line The active and reactive power in time period t, k is the index of the downstream child node of node j, is the set of downstream child nodes of node j, s is the element index in the set, is the set of electric vehicles connected to node j during period t, 、 are the active and reactive outputs of the electric vehicle at node j, is the set of photovoltaics connected to node j during period t, 、 are the photovoltaic active and reactive outputs at node j, 、 is the Lagrange multiplier corresponding to the equality constraint, T is the time period set, N is the node set, is the set of nodes except the root node, is the only upstream parent node of node j, 、 are the upper and lower limits of the square of the voltage amplitude, is the upper limit of the square of the current amplitude, and L is the line set.

[0093] In addition, based on the consideration of power flow, the transformer thermal aging model is introduced to consider the thermal aging cost of the transformer. The transformer aging acceleration factor is related to the hottest point temperature of the winding and can be expressed by the following linearized approximate formula:

[0094] ;

[0095] The hottest point temperature of the winding can be expressed as:

[0096] ;

[0097] The dynamic equation of top oil temperature is:

[0098] (9)

[0099] Among them, among them, is the aging acceleration factor of the heat exchanger in time period t, M represents the total number of segments, 、 Respectively The slope and intercept of the segment, is the hottest point temperature of the winding during period t, For the The lower limit of the temperature range of the segment, For the The upper limit of the temperature range of the segment, is the top oil temperature at time t, m is a constant, is the temperature rise of the hottest point of the winding compared to the top oil temperature under rated load, is the square of the current amplitude, is the square of the rated current amplitude, is the Lagrange multiplier corresponding to the constraint, which is used to calculate the impact of the load at the end of a working cycle T on the transformer life loss in the next working cycle. e is the index of the transformer in working state, and E is the set of transformers in working state. is the temperature rise of the top oil temperature compared to the ambient temperature at rated load, It can be defined as the ratio of the square of the current amplitude to the square of the rated current amplitude, that is, , R is the ratio of load loss under rated load to no-load loss. is the transformer oil time constant, and the commonly recommended value is 3h. and n are constants with recommended values ​​of 1 and 0.8 respectively. Since the scheduling period in this paper is 1 hour, we can perform differential processing on Equation (9) to obtain:

[0100] ;

[0101] in, , The calculation formula is as follows

[0102] ;

[0103] Here, m is a constant with a recommended value of 0.8. Therefore, Can be Taylor expansion of the variables around the value 1 gives an approximate linear equation. Similarly, A similar approximation can also be made based on Taylor expansion to obtain:

[0104] ;

[0105] Substituting equations (12) and (13) into equations (10) and (11), we can obtain:

[0106] ;

[0107] Substituting formula (15) into formula (8), we can get

[0108] ;

[0109] According to formula (14) and formula (16), the hottest point temperature of the winding can be calculated. That is, the relationship between the aging acceleration factor and the square of the current amplitude is obtained. In order to ensure that the optimal power flow problem can be solved cyclically, it is necessary to add constraints, and its Lagrange multiplier is Characterize the impact of the transformer load at the end of a working cycle T on the transformer life loss in the next working cycle:

[0110] ;

[0111] At this point, the transformer thermal aging model is completed.

[0112] In addition, in this embodiment, for the active power distribution network, the access of two distributed energy sources, electric vehicles and distributed photovoltaics, is considered.

[0113] The first operating model of an electric vehicle can be expressed as follows:

[0114] ;

[0115] in, To meet the charging needs of electric vehicles, is the maximum limit of the active power of the electric vehicle, is the maximum limit of the apparent power of the electric vehicle. Equations (19) and (20) impose maximum limits on the active power and apparent power of the electric vehicle, respectively.

[0116] For distributed photovoltaics, the second operating model can be expressed as:

[0117] ;

[0118] in, is the maximum limit of distributed photovoltaic active power, is the maximum limit of the distributed photovoltaic apparent power. Equations (21) and (22) impose maximum limits on the active power and apparent power of distributed photovoltaics, respectively.

[0119] Step S102: constructing an optimization problem model on the network side, the EV side, and the PV side based on the steady-state branch power flow model, the transformer thermal aging model, the first operation model, and the second operation model;

[0120] It should be pointed out that for a given EV / PV scheduling strategy, that is, 、 、 、 , the active / reactive power balance constraint of each node, that is, Equation (1) and Equation (2) are transformed into:

[0121] ;

[0122] For the network side, the final objective function is:

[0123] ;

[0124] in, 、 、 are the price of active power purchased from the root node, the unit cost of reactive power, and the unit cost of transformer life loss, is the active power of line 01 in period t, is the reactive power of line 01 during period t.

[0125] Therefore, the network optimization problem model on the network side includes: minimizing the objective function , the constraints include Equation (23)-Equation (24), Equation (3)-Equation (7), Equation (14), Equation (16)-Equation (17).

[0126] For EV, each EV will be charged according to the node active / reactive power price. Adjust the output curve Thus, the initial objective function of EV is:

[0127] ;

[0128] Therefore, the EV optimization problem model can include: minimizing the objective function , the constraint condition is - .

[0129] At this point, the EV optimization problem model is obtained. In this model, when When it is a negative value, EV has the opportunity to sell active and reactive power to the grid to earn revenue, and to charge during periods of low electricity prices to ensure normal use by users.

[0130] For PV, each PV will be charged according to the node active / reactive power price. Adjust the output curve Thus maximizing its own benefits. Therefore, the initial objective function of PV is:

[0131] ;

[0132] Therefore, the PV optimization problem model includes: minimizing the objective function , the constraint condition is -Mode .

[0133] So far we have obtained the PV optimization problem model. In this model, when season .when When PV is used, it can adjust the inverter power factor to provide reactive power to the grid, thus ensuring .

[0134] At this point, the network optimization problem model, EV optimization problem model and PV optimization problem model are obtained.

[0135] Step S103: defining a first decision variable related to the active distribution network and a second decision variable related to electric vehicles and photovoltaics, and generating an optimal power flow problem for the active distribution network and a first optimization problem related to the second decision variable based on the definition results;

[0136] It should be noted that in this step, the definition is a first decision variable related to the power distribution network, wherein the first decision variable includes 、 、 、 、 、 、 ;definition is a second decision variable related to electric vehicles and photovoltaics, and the second decision variable includes 、 、 、 Based on this, the optimal power flow problem of active distribution network can be expressed in the following compact form:

[0137] ;

[0138] in, represents the operating cost of the distribution network, represents the operating cost associated with EV / PV, It represents the relationship constraints between the decision variables related to the distribution network and the decision variables related to electric vehicles and photovoltaics. middle is the feasible region of the first decision variable, middle is the feasible region of the second decision variable. In addition, it should be noted that the motivation for coordinated operation between ADN operators and distributed energy operators is to maximize their own profits while meeting load demand safely and stably, or equivalently minimize operating costs. The solution to the traditional optimal power flow problem requires the assumption that there is a subject that is not profit-oriented, and a centralized optimization algorithm is used to solve this complex non-convex optimization problem. The present invention will propose a distributed algorithm to solve the optimal power flow problem, and does not require the existence of such a high-level subject, nor does it require the ADN operator to have ownership or control of distributed energy, but rather through node electricity prices. Distributed energy operators are encouraged to collaborate with ADN operators. Based on the node electricity prices broadcast by the ADN operator, each distributed energy operator optimizes its own profits and reports the adjusted output curve to the ADN operator. Based on the latest output information received, the ADN operator further optimizes the distribution network flow, thus facing the following first optimization problem:

[0139] ;

[0140] in, represents the value of a decision variable that is known to be correlated with the second decision variable, is the node electricity price matrix, , is the matrix that aggregates the node active power prices of all nodes in all time periods. is the matrix that aggregates the node reactive power prices of all nodes in all time periods. A, B, and D are the coefficient matrices of the equality constraints related to the second decision variable in the steady-state branch power flow model. To meet the relevant conditions. Combine Equation (28)-Equation (31) and the distribution network decision variables The optimal value of , then there is the following optimization problem about y:

[0141] ;

[0142] It should be noted that when the dual decomposition algorithm and the alternating direction method of multipliers (ADMM) do not converge, the equality constraints The solution does not satisfy the safety constraint and is not executable. Compared with the commonly used dual decomposition algorithm and ADMM algorithm, the first optimization problem is constructed to ensure that the equality constraint is satisfied in each iteration. This means that even if the algorithm is interrupted before convergence, the suboptimal solution it obtains can still be used as a scheduling instruction.

[0143] Since the dual variables obtained by solving equations (32)-(34) are Corresponding to exist The gradient value at . Therefore, Equations (28)-(31) can be solved using an iterative method. This iterative method can update the variable y by solving the gradient obtained by Equations (32)-(34). Here, a proximal algorithm is used to update the variable y. Let Indicates in The value of the iteration, given , solve the first optimization problem; then based on the first optimization problem, we get the optimization problem about y, and solve the optimization problem about y to get :

[0144] ;

[0145] make , by solving equations (32)-(34), we can get . is a positive scalar parameter.

[0146] Then, for a given 、 , the final objective function of the EV side is:

[0147] ;

[0148] For a given 、 , the final objective function on the PV side is:

[0149] .

[0150] Step S104: introducing an inverse inequality function for the constraint condition, performing linear processing on the inverse inequality function, and introducing a penalty term to obtain a second optimization problem;

[0151] It should be noted that in this embodiment, the constraint condition corresponds to Equation (4), which uses a second-order cone relaxation constraint representation. This inexact relaxation may lead to the provision of erroneous price signals. Here, the error of the kth iteration of the inexact relaxation is defined as for:

[0152] ;

[0153] Therefore, in order to ensure the equality, we need to add an inverse inequality function:

[0154] ;

[0155] This formula is equivalent to:

[0156] ;

[0157] Since inequality (43) is too complicated, we can consider linearizing the function on the right side of the inequality sign and introducing a penalty term, so we have:

[0158] ;

[0159] At the same time, the penalty item Apply the weight factor of the mth inner loop By continuously adjusting the weight factor And solve the following second optimization problem, which can continuously reduce the relaxation error :

[0160] ;

[0161] The constraints of the second optimization problem include equations (23)-(24), (3)-(7), (14), (16)-(17), and (44)-(45).

[0162] When the error When the error tolerance is less than or equal to a predetermined value, the constraint (4) in the network optimization problem model needs to be replaced by the following equation to obtain the operating point that follows the law of conservation of energy:

[0163] .

[0164] In summary, the second-order cone relaxation introduced for the DistFlow model to achieve the non-convex equality constraint of apparent power is not an exact relaxation, which has some error. Therefore, a reverse inequality is considered to be added, and a linearization process is designed for the reverse inequality and a penalty term is introduced. At the same time, an inner loop is designed to gradually reduce the relaxation error.

[0165] Step S105: recursively solve the optimization problem models of the network side, the EV side and the PV side according to the improved solving algorithm.

[0166] It should be pointed out that in some embodiments, the improved solving algorithm flow is specifically as follows:

[0167] Step 1, from start, given the initial scheduling strategy, including , , , , set the error tolerance , the iteration time limit ;

[0168] Step 2: solve the network optimization problem model of the network side according to the given initial scheduling strategy, which includes: solving the final objective function of the network side, that is, minimizing the formula , the constraint conditions include formula (23)-formula (24), formula (3)-formula (7), formula (14), formula (16)-formula (17);

[0169] Step 2.1: if the constraint condition formula (4) is an active constraint, the correct and are obtained, and step 3 is entered;

[0170] Step 2.2, if the constraint condition is an inactive constraint, set the initial inner loop number ;

[0171] Step 2.3, solve the problem model with the second optimization problem as the objective function, that is, formula (46), and formula (23)-formula (24), formula (3)-formula (7), formula (14), formula (16)-formula (17), formula (44)-formula (45) as the constraint condition, and judge whether the error of the kth iteration is greater than ;

[0172] Step 2.4, if , let , return to step 2.3 for continuous solving;

[0173] Step 2.5, if , solve the new network optimization problem model, and obtain and , go to step 3, the new network optimization problem model is obtained by replacing equation (4) in the network optimization problem model on the network side with equation (47);

[0174] Step 3: Get and , solve the network optimization problem model on the EV side separately, the network optimization problem model on the EV side includes: minimizing the final objective function on the EV side, that is, minimizing formula (39), and the constraints include formula (18)-formula (20); and solve the network optimization problem model on the PV side separately, the network optimization problem model on the PV side includes: minimizing the final objective function on the PV side, that is, minimizing formula (40), and the constraints include formula (21)-formula (22), obtain the scheduling strategy under the current iteration, and judge whether the scheduling strategy output by the current iteration meets the demand or whether the current iteration reaches the iteration time;

[0175] Step 4: If the requirements are met or the iteration time is reached, the iteration ends. If the requirements are not met and the iteration time is not reached, the scheduling strategy output by the previous iteration is used as the new given scheduling strategy, and the process returns to step 2. Meeting the requirements means that the obtained scheduling strategy is feasible.

[0176] Compared with the prior art, the present invention has the following advantages:

[0177] 1. The present invention constructs a steady-state branch flow model of the active distribution network, a transformer thermal aging model, and an operation model for electric vehicles and distributed photovoltaics, and based on these models, constructs an optimization problem model covering the network side, EV side, and PV side. At the same time, the decision variables on different sides are defined and converted into optimization problems. The constraints are then processed and linearized with special functions and penalty terms are introduced to form the final optimization problem. Finally, an improved solution algorithm is used for recursive solution. This hierarchical modeling and optimization solution method naturally fits the distributed computing architecture. Different models and optimization problems can be processed in parallel by different computing nodes in a distributed computing environment, thereby realizing distributed computing. In the processing of each link, reasonable data partitioning and model abstraction are used, and the key parameters required for optimization solution are transmitted only when necessary, avoiding direct interaction of sensitive data, ensuring personal privacy and the independent computing needs of each participant.

[0178] 2. In addition to considering the cost of purchasing electricity, the present invention also considers the cost of transformer aging. In addition, this method establishes an inverse inequality for the error caused by the second-order cone relaxation, and through linearization processing and inner loop iteration, it can gradually reduce the relaxation error while being easy to optimize. At the same time, this method can ensure the executability of the temporary solution obtained in the iterative process, and also makes it possible to dynamically adjust the target parameters and constraints during each iteration to cope with the real-time changing system status. When violations are observed, the optimization problem parameters can be adjusted at any time. In actual engineering, dispatchers will follow a similar iterative update process while ensuring the feasibility of the solution. The scheduling plan is checked. If it is found that some constraints that have not been formalized are not met, these constraints are incorporated into the standardized optimization problem, or the original parameters / constraints are adjusted to ensure feasibility. In day-ahead scheduling, this process will be repeated until a feasible solution is found or time is exhausted.

[0179] like Figure 2 As shown, the present invention also proposes a distributed power flow calculation system for an active power distribution network, the system comprising:

[0180] The power flow model construction module 10 is used to construct a steady-state branch power flow model of the active distribution network and its corresponding constraints, construct a transformer thermal aging model, define electric vehicles and distributed photovoltaics as energy access types of the active distribution network, and construct a first operation model and a second operation model for electric vehicles and distributed photovoltaics respectively;

[0181] An optimization problem model construction module 20 is used to construct an optimization problem model on the network side, the EV side, and the PV side based on the steady-state branch power flow model, the transformer thermal aging model, the first operation model, and the second operation model;

[0182] A decision variable definition module 30 is used to define a first decision variable related to the active distribution network and a second decision variable related to electric vehicles and photovoltaics, and generate an optimal power flow problem for the active distribution network and a first optimization problem related to the second decision variable based on the definition results;

[0183] an optimization problem construction module 40, configured to introduce an inverse inequality function into the constraint condition, perform linear processing on the inverse inequality function, and introduce a penalty term to obtain a second optimization problem;

[0184] The recursive solution module 50 is used to recursively solve the optimization problem models on the network side, the EV side, and the PV side according to the improved solution algorithm.

[0185] On the other hand, the present invention further provides a storage medium on which one or more programs are stored. When the programs are executed by a processor, the above-mentioned distributed power flow calculation method for the active power distribution network is implemented.

[0186] On the other hand, the present invention further provides an electronic device including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the above-mentioned active power distribution network distributed power flow calculation method.

[0187] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0188] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0189] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0190] While the embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations of these embodiments are possible. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as set forth in the claims. Furthermore, the invention described herein is susceptible to other embodiments and may be practiced or implemented in a variety of ways.

Claims

1. A distributed power flow calculation method for an active power distribution network, characterized in that: The method comprises: Construct a steady-state branch power flow model of the active distribution network and its corresponding constraints, build a transformer thermal aging model, define electric vehicles and distributed photovoltaics as energy access types for the active distribution network, and construct the first and second operation models for electric vehicles and distributed photovoltaics respectively; Constructing an optimization problem model on the network side, the EV side, and the PV side based on the steady-state branch power flow model, the transformer thermal aging model, the first operation model, and the second operation model; For the network side, the final objective function is: ； in, 、 、 are the price of active power purchased from the root node, the unit cost of reactive power, and the unit cost of transformer life loss, is the active power of line 01 in period t, is the reactive power of line 01 during period t, is the aging acceleration factor of the heat exchanger in period t; defining a first decision variable related to the active distribution network and a second decision variable related to electric vehicles and photovoltaics, and generating an optimal power flow problem of the active distribution network and a first optimization problem related to the second decision variable based on the definition results; definition is a first decision variable related to the power distribution network, wherein the first decision variable includes 、 、 、 、 、 、 , are respectively the active and reactive power of the line in period t, 、 are the squares of the voltage amplitudes at nodes j and i during time period t, For the line The square of the current amplitude during the period t, is the top oil temperature during period t; definition is a second decision variable related to electric vehicles and photovoltaics, and the second decision variable includes 、 、 、 , 、 are the active and reactive outputs of the electric vehicle at node j, is the set of photovoltaics connected to node j during period t, 、 are the photovoltaic active and reactive outputs at node j respectively; The first optimization problem is formulated as follows: ； in, represents the distribution network operating cost, is the feasible region of the first decision variable, is the feasible region of the second decision variable, is the node electricity price matrix, A, B, and D are the coefficient matrices of the equality constraints related to the second decision variable in the steady-state branch power flow model, is the value of the decision variable known to be correlated with the second decision variable, To meet relevant conditions; Given , solve the first optimization problem; Based on the first optimization problem, we get the optimization problem about y, and solve the optimization problem about y to get : ； in, 、 are the values ​​solved for the kth and k+1th iterations respectively, To obtain the variable value that minimizes the objective function, represents the operating costs associated with electric vehicles and photovoltaics, is the transpose of the node electricity price matrix at the kth iteration; definition 、 is the active and reactive power output of photovoltaic power in time period t at the kth iteration, 、 are the active and reactive outputs of the electric vehicle in the t period at the kth iteration, respectively. The final objective function of the EV side is: ； The final objective function on the PV side is: ； in, is a positive scalar parameter at the kth iteration, is the node active electricity price in period t at the kth iteration, is the node reactive power price in period t at the kth iteration; Introducing an inverse inequality function for the constraint condition, performing linear processing on the inverse inequality function, and introducing a penalty term to obtain a second optimization problem; The improved solution algorithm is used for recursive solution, as follows: Step 1: From Start by giving the initial scheduling strategy, including 、 、 、 , set the error tolerance , iteration time limit ; Step 2: Solve the final objective function on the network side based on the given initial scheduling strategy and determine whether the constraints are effective. Step 2.1: If the constraint is a valid constraint, get the correct and , go to step 3; Step 2.2: If the constraint condition is invalid, set the initial number of inner loops. ; Step 2.3: Solve the second optimization problem and determine the error of the kth iteration. Is it greater than ; Step 2.4: If ,make , return to step 2.3 to continue solving; Step 2.5: If , solve the new network optimization problem model, and obtain and , go to step 3, the new network optimization problem model includes the following expression: ； in, 、 is the line obtained in the kth iteration Active and reactive power in period t, is the square of the voltage amplitude of node i in time period t obtained in the kth iteration, T is a set consisting of the index of the optimization time period, is the set of nodes except the root node, It is the only upstream parent node of node j; Step 3: Get and , solve the final objective function of the EV side and the PV side, obtain the scheduling strategy under the current iteration, and judge whether the scheduling strategy output by the current iteration meets the demand or whether the current iteration reaches the iteration time; Step 4: If the requirements are met or the iteration time is reached, the iteration ends. If the requirements are not met and the iteration time is not reached, the scheduling strategy output by the previous iteration is used as the new given scheduling strategy and returns to step 2.

2. The method for calculating distributed power flow in active power distribution network according to claim 1, characterized in that: The steps of constructing a steady-state branch power flow model of the active distribution network and corresponding constraints include: The steady-state branch power flow model is constructed according to the following formula: ； in, 、 Line The resistance and reactance on 、 The active and reactive power of the load of node j in time period t, 、 Line The active and reactive power in time period t, k is the index of the downstream child node of node j, is the set of downstream child nodes of node j, s is the element index in the set, is the set of electric vehicles connected to node j during period t, 、 is the Lagrange multiplier corresponding to the equality constraint, T is the time period set, and N is the node set; The constraints are constructed according to the following formula: ； in, 、 are the upper and lower limits of the square of the voltage amplitude, is the upper limit of the square of the current amplitude, and L is the line set.

3. The method for calculating distributed power flow in active power distribution network according to claim 2, characterized in that: The steps of constructing a transformer thermal aging model, defining electric vehicles and distributed photovoltaics as energy access types for the active distribution network, and constructing a first operation model and a second operation model for electric vehicles and distributed photovoltaics respectively include: The transformer thermal aging model is constructed according to the following formula: ； Where M represents the total number of segments, 、 Respectively The slope and intercept of the segment, is the hottest point temperature of the winding during period t, For the The lower limit of the temperature range of the segment, For the The upper limit of the temperature range of the segment, m is a constant, is the temperature rise of the hottest point of the winding compared to the top oil temperature under rated load, is the square of the current amplitude, is the square of the rated current amplitude, is the Lagrange multiplier corresponding to the constraint, which is used to express the influence of the load at the end of one working cycle T on the life loss of the transformer in the next working cycle, e is the index of the transformer in working state, and E is the set of transformers in working state; The first run model is constructed according to the following formula: ； in, To meet the charging needs of electric vehicles, is the maximum limit of the active power of the electric vehicle, The maximum limit of the apparent power of the electric vehicle; The second run model is constructed according to the following formula: ； in, is the maximum limit of distributed photovoltaic active power, It is the maximum limit of distributed photovoltaic apparent power.

4. The method for calculating distributed power flow in active power distribution network according to claim 3, characterized in that: The step of constructing an optimization problem model on the network side, the EV side, and the PV side based on the steady-state branch power flow model, the transformer thermal aging model, the first operation model, and the second operation model includes: For the EV side, the initial objective function is: ； For the PV side, the initial objective function is: ； in, is the node active power price in period t, is the node reactive power price in period t.

5. The method for calculating distributed power flow in active power distribution network according to claim 4, characterized in that: The steps of introducing an inverse inequality function for the constraint condition, performing linear processing on the inverse inequality function, and introducing a penalty term to obtain the second optimization problem include: The expression of the introduced reverse inequality function is as follows: ； The reverse inequality function is linearly processed and a penalty term is introduced to obtain: ； in, is the line obtained in the kth iteration The square of the current amplitude during the period t, For penalty items; Penalty items Apply weighting factors , we get the second optimization problem: 。 6. A distributed power flow calculation system for an active power distribution network, characterized in that: The system comprises: The power flow model construction module is used to construct the steady-state branch power flow model of the active distribution network and its corresponding constraints, build a transformer thermal aging model, define electric vehicles and distributed photovoltaics as energy access types for the active distribution network, and construct the first and second operation models for electric vehicles and distributed photovoltaics respectively; An optimization problem model construction module, configured to construct optimization problem models on the network side, the EV side, and the PV side based on the steady-state branch power flow model, the transformer thermal aging model, the first operation model, and the second operation model; For the network side, the final objective function is: ； in, 、 、 are the price of active power purchased from the root node, the unit cost of reactive power, and the unit cost of transformer life loss, is the active power of line 01 in period t, is the reactive power of line 01 during period t, is the aging acceleration factor of the heat exchanger in period t; A decision variable definition module is used to define a first decision variable related to the active distribution network and a second decision variable related to electric vehicles and photovoltaics, and generate an optimal power flow problem for the active distribution network and a first optimization problem related to the second decision variable based on the definition results; definition is a first decision variable related to the power distribution network, wherein the first decision variable includes 、 、 、 、 、 、 , are respectively the active and reactive power of the line in period t, 、 are the squares of the voltage amplitudes at nodes j and i during time period t, For the line The square of the current amplitude during the period t, is the top oil temperature during period t; definition is a second decision variable related to electric vehicles and photovoltaics, and the second decision variable includes 、 、 、 , 、 are the active and reactive outputs of the electric vehicle at node j, is the set of photovoltaics connected to node j during period t, 、 are the photovoltaic active and reactive outputs at node j respectively; The first optimization problem is formulated as follows: ； in, represents the distribution network operating cost, is the feasible region of the first decision variable, is the feasible region of the second decision variable, is the node electricity price matrix, A, B, and D are the coefficient matrices of the equality constraints related to the second decision variable in the steady-state branch power flow model, is the value of the decision variable known to be correlated with the second decision variable, To meet relevant conditions; Given , solve the first optimization problem; Based on the first optimization problem, we get the optimization problem about y, and solve the optimization problem about y to get : ； in, 、 are the values ​​solved for the kth and k+1th iterations respectively, To obtain the variable value that minimizes the objective function, represents the operating costs associated with electric vehicles and photovoltaics, is the transpose of the node electricity price matrix at the kth iteration; definition 、 is the active and reactive power output of photovoltaic power in time period t at the kth iteration, 、 are the active and reactive outputs of the electric vehicle in the t period at the kth iteration, respectively. The final objective function of the EV side is: ； The final objective function on the PV side is: ； in, is a positive scalar parameter at the kth iteration, is the node active electricity price in period t at the kth iteration, is the node reactive power price in period t at the kth iteration; an optimization problem construction module, configured to introduce an inverse inequality function into the constraint condition, perform linear processing on the inverse inequality function, and introduce a penalty term to obtain a second optimization problem; The recursive solution module is used to perform recursive solution based on the improved solution algorithm, as follows: Step 1: From Start by giving the initial scheduling strategy, including 、 、 、 , set the error tolerance , iteration time limit ; Step 2: Solve the final objective function on the network side based on the given initial scheduling strategy and determine whether the constraints are effective. Step 2.1: If the constraint is a valid constraint, get the correct and , go to step 3; Step 2.2: If the constraint condition is invalid, set the initial number of inner loops. ; Step 2.3: Solve the second optimization problem and determine the error of the kth iteration. Is it greater than ; Step 2.4: If ,make , return to step 2.3 to continue solving; Step 2.5: If , solve the new network optimization problem model, and obtain and , go to step 3, the new network optimization problem model includes the following expression: ； in, 、 is the line obtained in the kth iteration Active and reactive power in period t, is the square of the voltage amplitude of node i in time period t obtained in the kth iteration, T is a set consisting of the index of the optimization time period, is the set of nodes except the root node, It is the only upstream parent node of node j; Step 3: Get and , solve the final objective function of the EV side and the PV side, obtain the scheduling strategy under the current iteration, and judge whether the scheduling strategy output by the current iteration meets the demand or whether the current iteration reaches the iteration time; Step 4: If the requirements are met or the iteration time is reached, the iteration ends. If the requirements are not met and the iteration time is not reached, the scheduling strategy output by the previous iteration is used as the new given scheduling strategy and returns to step 2.

7. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by a processor, implement the method for calculating distributed power flow in an active power distribution network according to any one of claims 1 to 5.

8. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the active power distribution network distributed power flow calculation method according to any one of claims 1 to 5.

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